Ambient-Assisted Senior Living with Disabilities in an Intelligent House using CNN Behavior Prediction

This paper presents a novel strategy for enhancing the quality of life for elderly disabled people living in smart homes: Ambient-Assisted Living (AAL) systems. We anticipate and comprehend their everyday actions and behaviors by utilizing Convolutional Neural Networks (CNN) and Closed-Circuit Television (CCTV) technology. We gather information from carefully placed CCTV cameras, which we then analyze using a CNN model to identify particular behaviors like meal preparation, medication administration, and mobility patterns. In order to maintain accuracy over time, the CNN model is adjusted to match the particular requirements of every resident. Elderly residents' safety and security are improved by the system's ability to provide real-time alerts and interventions in response to behavioral anomalies or deviations from expected patterns. Caregivers and family members can access an intuitive interface that makes it possible for remote monitoring and providing essential support. This strategy represents a major breakthrough in AAL, providing individualized care and significantly raising the standard of living for elderly residents with disabilities while simultaneously tackling the problems brought on by an aging population.